Robust decisions refer to choices made in statistical inference that remain effective and reliable under a variety of conditions or assumptions, particularly in the presence of uncertainty or variability. These decisions prioritize minimizing potential losses across different scenarios, which is crucial when dealing with incomplete information or model uncertainty. Robustness emphasizes the resilience of a decision-making process, ensuring that even if the underlying assumptions change, the outcomes remain acceptable.
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